Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping

• Published in: International journal of applied earth observation and geoinformation Vol. 112; p. 102854
• Main Authors: Shabanpour, Negar, Razavi-Termeh, Seyed Vahid, Sadeghi-Niaraki, Abolghasem, Choi, Soo-Mi, Abuhmed, Tamer
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2022; Elsevier
• Subjects: altitude; Cutaneous leishmaniasis; decision support systems; disease prevalence; Environmental factors; Health geography; humidity; Iran; Leishmania; Machine learning; prediction; rain; regression analysis; risk; risk assessment; spatial data; Spatial modelling; species; temperature; uncertainty; vegetation index; wind speed; Iran; Cutaneous leishmaniasis; Environmental factors; Spatial modelling; Machine learning; Health geography
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2022.102854

•Use of geospatial artificial intelligence (GeoAI) to spatially analyze a parasitic disease.•Spatial modeling of cutaneous leishmaniasis and its mapping using decision tree (DT), support vector regression (SVR), and linear regression (LR).•Assessing the accuracy of maps generated using three machine learning algorithms (SVR, DT, LR) showed that DT is the most accurate algorithm for predicting leishmaniasis.•The findings revealed that leishmaniasis is a high-risk area in Isfahan province's northern and central areas. Cutaneous leishmaniasis is a complex infection that is caused by different species of Leishmania and affects more than 2 million people in 88 countries. Identifying the environmental factors affecting the occurrence of cutaneous leishmaniasis and preparing a risk map is one of the basic tools to control and manage this disease. The aim of this study was a spatial prediction of cutaneous leishmaniasis in Isfahan province, Iran using three machine learning algorithms (decision tree (DT), support vector regression (SVR), and linear regression (LR)). The spatial database was created using data collected on the number of diseases in Isfahan province from 2011 to 2018, as well as ten environmental parameters (temperature, humidity, rainfall, altitude, slope, wind speed, normalized difference vegetation index (NDVI), number of sunny days, number of frosty days, and distance to stream) that affect the incidence of leishmaniasis. Furthermore, the fuzzy method was employed in this study to reduce uncertainty and evaluate the effect of environmental factors on disease prevalence. Using the holdout method and 70:30 ratios, the data were used to model and prepare a leishmaniasis prediction map and evaluate the results, respectively. The accuracy of the maps satisfied with the DT, SVR, and LR algorithms was 0.951, 0.934, and 0.914, respectively, according to the receiver operating characteristic (ROC) curve and area under the curve (AUC). Furthermore, the eastern and southern parts of the province have the lowest risk of leishmaniasis. The result of this issue is the identification of high-risk areas of the disease and increase life and peace for people in the community.